Liquids Level Calculation in Loaded Gas Wells from Surface Data Using Combined Analytical Modeling of Gaseous Static Liquids Gradient: Part 2
Bibliographic record
Abstract
Abstract As the second part of this series, we apply our much improved understanding of gas flow through static recirculating liquid columns and analytical countercurrent multiphase flow modeling to both conventional and unconventional horizontal gas well liquids loading and a deep dive of the process of wellbore liquids flow reversal post-loading. This part of our work focuses on a step change in understanding the aspect of multiphase flow that is most pertinent to artificial lift - countercurrent liquids loading and gas flow through liquid columns. It is shown that traditional concurrent flow principles and flow pattern maps used in prior commonly used flowing bottom hole pressure correlations do not apply and cannot explain the changing dual pressure gradient profiles in loaded gas wells as a result of flow reversal. Therefore, this work lays the foundation for a change in conversation and focus among the artificial lift community towards countercurrent and static liquid column multiphase flow behaviors prevalent in all liquids-producing gas wells. We show and field-validate a new computational ability to perform multiphase countercurrent liquids loading calculations that dynamically loads a gas well tubing/casing and the calculations of total pressure gradient that varies with the increasing gas holdup along the static liquid columns of these wells. Additionally, we analyze the process of countercurrent flow and put forward a redefinition of onset of liquids flow reversal in the proper context of prior studies in this field. Our results are used to simulate the liquid levels in loaded gas wells from only basic surface field data. This represents an advance towards low-cost, low-carbon gas well production optimization and the opportunity of simulation-based real-time downhole diagnostics to determine digital liquid levels and reliably accurate FBHP in loaded gas wells without the high-carbon costs of wellsite visits and equipment runs. In terms of reliable digital twin applications for gas wells producing liquids, our new method can be performed in an autonomous way on a wellhead - a sort of "gas well liquid level digital sensor" - a solution that takes advantage of available SCADA surface data and converts it to automated calculations of downhole pressures, flow rates and well liquid levels in response to dynamic well operating conditions. For the first time in the industry, we present in this work a simultaneous calculation of loaded gas well FBHP and gaseous liquids level from only surface data. In either cases of liquids loaded gas wells or pumping oil wells with gaseous liquid columns above them, the significant pressure gradient (delta-P) that gaseous liquid columns impose on the formation is of great importance in correctly understanding and analyzing well supply capacity and enhancing downhole production rates during production operations.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".